Fuzzy-ARTMAP based Multi-User Detection

Abstract

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.

Improvement of Pattern Recognition Capacity of the Fuzzy ART with the Variable Learning

“…가변 학습을 적용한 퍼지 ART [11] . 1] 범위의 값을 갖고 입력층에 제공되면 입력층은 식 (7)과 같은 상보부호화 입력벡터 I를 만든다 [2][3][4][5][6][7][8][9][10][11] .…”

“…[2][3][4][5][6][7][8][9] . 이런 신 경회로망을 다양한 분야에서 이용하는 연구가 꾸준 히 진행되고 있다 [10] . 장 큰 값을 갖는 노드를 선택한다 [2][3][4][5][6][7][8][9][10][11] .…”

Improvement of Properties of the Fuzzy ART with the Variable Weighed Average Learning

“…징을 가지고 있다 [2] . 그로스버그의 퍼지 ART는 학습 방법으로 고속학습(FL : fast learning), 고속수용저속 부호화(FCSR : Fast-Commit Slow-Record), 저속학 습(SL : Slow Learning) 방법이 있다 [2][3][4][5][6][7][8][9][10] . [2][3][4][5][6][7][8][9][10] .…”